Project Overview¶
- Goal: Construct a model that accurately predicts whether an individual makes more than 50k/yr
- Motivation: Help a non-profit identify donor candidates and understand how large of a donation to request
- Data Source: 1994 US Census Data UCI Machine Learning Repository*
Note: Datset donated by Ron Kohavi and Barry Becker, from the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". Small changes to the dataset have been made, such as removing the
'fnlwgt'feature and records with missing or ill-formatted entries.
EDA: Data Dictionary¶
- age: continuous.
- workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
- education_level: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
- education-num: continuous.
- marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
- occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
- relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
- race: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other.
- sex: Female, Male.
- capital-gain: continuous.
- capital-loss: continuous.
- hours_per-week: continuous.
- native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
Data Engineering/Preprocessing¶
Before this data can be used for modeling and application to machine learning algorithms, it must be cleaned, formatted, and structured.
Eng: Factor Names¶
Factor names with special characters, like -, can cause issues, so a cleaning may prove helpful.
Eng: Categorical Transformations¶
Working with categorical variables often involves transforming strings to some other value, frequently 0 or 1 for binomial factors, and {X = x_{0}, x_{1}, ..., x_{n} | 0, 1, .. n} multinomial.
These values may be ordinal (i.e. values with relationships that can be compared as a ranking, e.g. worst, better, best), or nominal (i.e. values indicate a state, e.g. blue, green, yellow).
Eng: Data Separation¶
For training an algorithm, it is useful to separate the label, or dependent variable ($Y$) from the rest of the data training_features, or independent variables ($X$).
Skew¶
The features capital_gain and capital_loss are positively skewed (i.e. have a long tail in the positive direction).
To reduce this skew, a logarithmic transformation, $\tilde x = \ln\left(x\right)$, can be applied. This transformation will reduce the amount of variance and pull the mean closer to the center of the distribution.
Why does this matter: The extreme points may affect the performance of the predictive model.
Why care: We want an easily discernible relationship between the independent and dependent variables; the skew makes that more complicated.
Why DOESN'T this matter: The distribution of the independent variables is not an assumption of most models, but the distribution of the residuals and homoskedasticity of the independent variable, given the independent variables, $E\left(u | x\right) = 0$ where $u = Y - \hat{Y}$ is of linear regression. In this analysis, the dependent variable is categorical (i.e. discrete or non-continuous) and linear regression is not an appropriate model.
The logarithmic transformation reduced the skew and the variance of each factor.¶
| Feature | Skewness | Mean | Variance |
|---|---|---|---|
| Capital Loss | 4.516154 | 88.595418 | 163985.81018 |
| Capital Gain | 11.788611 | 1101.430344 | 56345246.60482 |
| Log Capital Loss | 4.271053 | 0.355489 | 2.54688 |
| Log Capital Gain | 3.082284 | 0.740759 | 6.08362 |
Eng: Normalization and Standardization¶
These two terms, normalization and standardization, are frequently used interchangably, but have two different scaling purposes.
- Normalization: scale values between 0 and 1
- Standardization: transform data to follow a normal distribution, i.e. $X \sim N\left(\mu=0,\sigma^{2}=1\right)$
Earlier, capital_gain and capital_loss were transformed logarithmically, reducing their skew, and affecting the model's predictive power (i.e. ability to discern the relationship between the dependent and independent variables).
Another method of influencing the model's predictive power is normalization of independent variables which are numerical. Whereafter, each featured will be treated equally in the model.
However, after scaling is applied, observing the data in its raw form will no longer have the same meaning as before.